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FOXO1 promotes HIV latency by suppressing ER stress in T cells

Nature Microbiology volume 5, pages 1144–1157 (2020)Cite this article

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Abstract

Quiescence is a hallmark of CD4+ T cells latently infected with human immunodeficiency virus 1 (HIV-1). While reversing this quiescence is an effective approach to reactivate latent HIV from T cells in culture, it can cause deleterious cytokine dysregulation in patients. As a key regulator of T-cell quiescence, FOXO1 promotes latency and suppresses productive HIV infection. We report that, in resting T cells, FOXO1 inhibition impaired autophagy and induced endoplasmic reticulum (ER) stress, thereby activating two associated transcription factors: activating transcription factor 4 (ATF4) and nuclear factor of activated T cells (NFAT). Both factors associate with HIV chromatin and are necessary for HIV reactivation. Indeed, inhibition of protein kinase R-like ER kinase, an ER stress sensor that can mediate the induction of ATF4, and calcineurin, a calcium-dependent regulator of NFAT, synergistically suppressed HIV reactivation induced by FOXO1 inhibition. Thus, our studies uncover a link of FOXO1, ER stress and HIV infection that could be therapeutically exploited to selectively reverse T-cell quiescence and reduce the size of the latent viral reservoir.

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Fig. 1: FOXO1 is a specific regulator of HIV latency establishment.
Fig. 2: FOXO1 inhibition reactivates HIV1 from latency.
Fig. 3: FOXO1 inhibition prevents latency establishment and reactivates HIV in primary CD4+ T cells and HIV-infected CD4+ T cells.
Fig. 4: Marked upregulation of ER stress in response to FOXO1 inhibition in primary CD4+ T cells.
Fig. 5: FOXO1 inhibition induces HIV reactivation in the absence of NF-κB recruitment via ATF4 and NFAT.
Fig. 6: Induction of ER stress promotes HIV reactivation.

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Data availability

The complete array datasets of the RNA-seq data associated with Fig. 4a,b and Extended Data Fig. 4a are available at the Gene Expression Omnibus with the accession number GSE129522. The data that support the findings of the present study are available from the corresponding author upon request. Source data for Figs. 1, 4 and 5 are provided with this paper.

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Acknowledgements

We thank all members of the Ott, Verdin and Pillai laboratories for helpful discussions, reagents and expertise. We thank D. Lacuadra and A. Patel for assistance, M. Cavrois, N. Raman and the Gladstone Flow Cytometry Core for assistance with FACS, N. Carli, J. McGuire and the Gladstone Genomics Core for assistance with RNA-seq, J. Carroll for graphics, K. Claiborn and B. Mensh for editorial assistance, and V. Fonseca and L. Weiser for administrative assistance. This work was supported by the National Institutes of Health/National Institute of Allergy and Infectious Diseases (NIH/NIAID, grant nos. DP1DA038043 and R37AI083139 to M.O.), the NIH/National Institute on Drug Abuse (grant nos. R01DA041742 and R01AI117864 to E.V.), the NIH/National Institute of General Medical Sciences (grant no. R01GM117901 to S.K.P.) and the NIH (grant no. P30 AI027763 to Flow Cytometry Core). Research was also supported as part of the amfAR Institute for HIV Cure Research, with funding from amfAR (grant no. 109301). D.B. was also funded by the Gilead HIV Cure Mentored Scientist Award from the amfAR Institute for HIV Cure Research at the UCSF AIDS Research Institute.

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Author notes

  1. These authors contributed equally: Irene P. Chen, Rosalba Perrone.

Authors and Affiliations

  1. Gladstone Institute of Virology and Immunology, Gladstone Institutes, San Francisco, CA, USA

    Albert Vallejo-Gracia, Irene P. Chen, Daniela Boehm, Karsten Krey, Philip A. Hull, Ireneusz Habrylo & Melanie Ott

  2. University of California San Francisco, San Francisco, CA, USA

    Albert Vallejo-Gracia, Irene P. Chen, Daniela Boehm, Ireneusz Habrylo, Steven Deeks, Satish Pillai, Eric Verdin & Melanie Ott

  3. The Buck Institute for Research on Aging, Novato, CA, USA

    Rosalba Perrone, Emilie Besnard, Emilie Battivelli, Tugsan Tezil, Marius Walter, Andrew Cruz & Eric Verdin

  4. Ludwig Maximilian University, Munich, Germany

    Karsten Krey

  5. Vitalant Research Institute, San Francisco, CA, USA

    Kyle A. Raymond & Satish Pillai

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Contributions

A.V.-G. and M.O. designed and guided the study. A.V.-G., I.C. and R.P. designed, performed and analysed most biochemical experiments. E.B. helped design the project. D.B. performed ChIP assays. E.B. helped with primary cell experiments. T.T. performed CETSAs. K.K., K.R., P.A.H., M.W., I.H. and A.C. performed some biochemical experiments. S.D. provided resources. S.P., E.V. and M.O. provided the use of their laboratories and helped with data interpretation. The manuscript was written by A.V.-G. and M.O.

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Correspondence to Melanie Ott.

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Extended data

Extended Data Fig. 1 Extended Data Fig 1.

a, Jurkat-derived NH7(Cas9) cells were treated with increasing concentrations of the FOXO1 inhibitor AS1842856 just after HIVGKO infection. After 3-4 days, the percentage of latent and productively infected cells were quantified by FACS. In left panel, percentage of productively or latently cells relative to the total infection rate (bars) and total cell viability (blue dotted line) of a representative experiment. In the right panel, ratios of productive versus latent populations relative to the total infection rate upon increasing concentrations of AS1842856 treatments. Data are represented as mean ± SD of triplicate values, representative of n = 3 independent experiments. b, Representative western blots of CETSA assays in FOXO1 and FOXO3 in the presence or absence of 100 nM AS1842856. c, FOXO1 and FOXO3 CETSA-melting curves upon the presence or absence of AS1842856 100 nM in K562(Cas9) cells. Band intensities obtained from western blot analysis were normalized to the highest western blot signal which has been set to 100 %. Relative FOXO-band intensities were plotted against corresponding incubation temperatures and a nonlinear least-squares regression fit was applied. Data represent the mean ± SD of n = 3 individual experiments. d, Cell viability of CETSA assay in FOXO1 experiments assessed by Trypan Blue exclusion. Data represent the mean ± SD of three individual experiments. e, FOXO1, FOXO3 and FOXO4 gene expression in KD-1A-FOXO1 K562(Cas9) cell line analyzed by RT-qPCR, normalized to RPL13A mRNA. Data represent mean ± SD of n = 3 individual experiments, except for FOXO1 (n = 4). f, Cell growth analysis of WT, FOXO1, FOXO3 and FOXO4 knockdowns K562(Cas9) cell lines. Data are represented as mean ± SD of triplicate values. g-i, Percentage of productive or latent cells relative to the total infection rate and cell viability upon increasing concentrations of AS1842856 treatment in the WT K562(Cas9) (g) or in the FOXO1-knockdown cell lines KD-1A (h) and KD-1B (i). Data are represented as mean ± SD of n = 2 independent experiments.

Extended Data Fig. 2 Extended Data Fig 2.

a, J-Lat cell lines 5A8, 6.3, 11.1 and 15.4 were treated with increasing concentrations of AS1842856 for 24, 48 and 72 h and HIV-GFP reactivation was analyzed by flow cytometry. HIV-GFP reactivation is reported as a Mean Intensity Fluorescence (MFI) of GFP-expressing cells. Data represent mean ± SD of n ≥ 3 independent experiments. 10 ng/mL TNFα was used as control. b, J-Lat cell lines 5A8, 6.3, 11.1 and 15.4 were treated for 72 h with increasing concentrations of both AS1842856 (Y-axis) and TNFα (X-axis) alone or in combination and analyzed by FACS. HIV-GFP reactivation is reported as a percentage of GFP-expressing cells (% GFP + cells) or c, Mean Intensity Fluorescence (MFI). Data represent mean of n = 3 independent experiments.

Extended Data Fig. 3 Extended Data Fig 3.

a, HIV reactivation was measured by luciferase activity and cell viability by flow cytometry assessed in CD4+ T cells purified from blood of healthy donors and infected with HIVNL4-3 Luciferase and letting them rest for 6 days before reactivation was induced with 10 µg/mL PHA + 100 U/mL IL-2 and 10 µg/mL αCD3 + 1 µg/mL αCD28 for 72 h, in the presence of raltegravir (30 μM). Data represent average ± SD of n ≥ 3 independent experiments. b, The cell surface CD69 and CD25 T cell activation markers were measured by FACS in CD4+ T cells upon AS1842856 treatment for 24, 48 and 72 h. 10 µg/mL αCD3 and 1 µg/mL αCD28 was used as control. Data is shown as mean of percentage of positive cells and as mean ± SD of n = 2 biological replicates.

Extended Data Fig. 4 Extended Data Fig 4.

a, Venn diagrams (top panels) comparing the up- and down-regulated genes from a recently published Affimmetrix microarray19 and RNA-Seq data presented here (Vallejo-Gracia et al.). GO Enrichment Analysis (Biological Processes and Cellular Components) from overlapped dysregulated genes from the two datasets. b, Confirmation of selected up- or down-regulated genes and pathways after 72 h AS1842856 treatment in CD4+ T cells of HIV-infected patients on antiretroviral-therapy with undetectable viral load by RT-qPCR, normalized to RPL13A mRNA. Data represent mean ± SD of n = 10 individual donors. c, The thresholded Mander’s correlation coefficients were determined and P value was calculated by unpaired Student’s t test. n = 90 cells per condition. Data are represented as mean ± SD.

Extended Data Fig. 5 Extended Data Fig 5.

Representative plots of intracellular calcium-flux kinetics in K562(Cas9) and Jurkat cell lines in the presence or absence of AS1842856 (100 nM). Cells were stained with a membrane permeable calcium sensor dye in PBS and stimulated by adding Ionomycin after 30 seconds resulting in an increase of fluorescence indicating a calcium mobilization from the ER. Representative experiments of n = 3 independent experiments.

Extended Data Fig. 6 Extended Data Fig 6.

a, J-Lat cell line A58 was treated with increasing concentrations of GCN2i (A-92) in combination with 1,000 nM AS1842856 (72 h) or 10 ng/mL TNFα (24 h). In the upper panel, HIV-GFP reactivation was analyzed by FACS and relativized to the control. In the lower panel, histogram plots of percent live cells for each drug treatment are shown. Data are represented by mean ± SD of n = 3 different experiments. b, Same experiment as in Extended Data Fig. 6a but treating cells with increasing concentrations of PKRi (Imidazolo-oxindole PKR inhibitor C16). Data are represented by mean ± SD of three different experiments. c, Same experiment as in Extended Data Fig. 6a but treating cells with increasing concentrations of PERKi (GSK2656157 / PERK inhibitor II) (top panels) or the highly specific PERK inhibitor (AMG PERK 44) (lower panels). Histogram plots of percent live cells for each drug treatment are shown. Data represent mean ± SD of n = 3 independent experiments. d, Same experiment as in Extended Data Fig. 6a, but cells were treated with increasing concentrations of IRE1αi (MKC8866). Data are represented by mean ± SD of n = 3 different experiments. e, J-Lat cell line A58 was treated with increasing concentrations of IRE1αi (MKC8866) in combination with 1 µM Thapsigargin (6 h). Data are represented by mean ± SD of n = 3 different experiments. f, Same experiment as in Extended Data Fig. 6a but treating cells with increasing concentrations of CsA (Cyclosporin A) (left panels) or the combined concentrations of PERKi and Cyclosporin A (right panels). Histogram plots of percent live cells for each drug treatment are shown. Data represent mean ± SD of n ≥ 3 independent experiments. g, J-Lat cell line A58 was treated with increasing concentrations of Thapsigargin (0.01, 0.1, 1 µM), Brefeldin A (0.01, 0.1, 1 µg/mL) and Fenretinide (0.5, 2, 5 µM) for 24, 48 and 72 h (bottom right). Histogram plots of percent live cells for each drug treatment are shown. Data represent mean ± SD of n = 3 independent experiments. h, J-Lat cell line A58 was treated with increasing concentrations of Ionomycin (0.01, 0.1, 0.5, 1 µM) for 24, 48 and 72 h and HIV-GFP reactivation (upper panel) and cell viability (lower panel) were analyzed by FACS. Data represent mean ± SD of n = 3 independent experiments. i, J-Lat cell line 5A8 was treated for 72 h with increasing concentrations of both Fenretinide (Y-axis) and Ionomycin (X-axis) alone or in combination and analyzed by FACS. HIV-GFP reactivation is reported as a percentage of GFP-expressing cells (% GFP + cells) (upper panel) and viability was measured by FACS (bottom panel). Data represent average of n = 3 independent experiments.

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Vallejo-Gracia, A., Chen, I.P., Perrone, R. et al. FOXO1 promotes HIV latency by suppressing ER stress in T cells. Nat Microbiol 5, 1144–1157 (2020). https://doi.org/10.1038/s41564-020-0742-9

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